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Find out if the most popular spreadsheet software in the world, Excel, is the right tool to perform data analysis.
If you’re wondering if data analysis can be done in Excel, the simple answer is yes.
But is Excel also the right tool for the job? Not really, or better said, it depends.
When we hear about “Excel”, we probably think of an ancient, outdated, and no longer relevant spreadsheet software. But that’s far from true.
Can Data Analysis Be Done in Excel?
Although Excel is a powerful tool for data analysis, it is recommended to use Excel only when working with small datasets. For large datasets, in order to avoid data quality issues in reporting, you should do your analysis in Python, SQL, or another appropriate language.
Access to advanced analysis features
While Microsoft Excel has been around for more than 30 years (released in 1985), it has been continuously improved.
Now it comes with modern BI features that now allow you to go beyond basics and execute complex statistical analysis, formulas, power queries, and even create data visualizations.
With functions like VLOOKUP, XLOOKUP, PIVOT TABLES, Index Match, and Analysis ToolPak, Excel allows you to clean, explore and visualize data.
Unmatched flexibility and ease of use
With a friendly GUI and a minimal learning curve, it quickly became the most popular analysis software in the world.
While this is a good thing, this can also be a disadvantage because people skip taking proper training and start using it right away. This heavily increases the chances of making mistakes having a negative impact on the quality of the analysis.
Perfect for quick data inspections
Spreadsheets are commonly used to inspect the results and check the correctness of the information. They bring clarity to projects when dealing with smaller and usually well-formatted datasets. Excel is also used often for quick calculations and ad hoc analysis requests.
While Excel is great – it has its limitations.
Not suitable for large datasets
Even with functions like VLOOKUP, XLOOKUP, PIVOT TABLES, Index Match, and Analysis ToolPak, Excel cannot handle large datasets, lacks the possibility to automate processes, and things can get cluttered and extremely slow.
Business analysts usually deal with big data and rely on a combination of various programming languages and fast technologies like SAS, SQL, R, Python, Tableau, PowerBi, and use Excel more like a side solution.
88% of Spreadsheets Contain Errors
Excel is undervalued and overlooked. That’s why most people don’t consider professional training in Excel, and never end up using it at its full value. Because it’s easily accessible to everyone, people tend to learn it on the go, which raises the rate of making mistakes.
According to this article by Market Watch, almost 90% of spreadsheets contain some type of errors.
Because you need to do everything in one place, cleaning, preparing, and analyzing the data, it can become very difficult to follow the logic behind the analysis. With formulas hidden in cells, you can accidentally overwrite them or input wrong information. This leads to small mistakes having big consequences.
It’s also quite difficult to find those errors especially if that spreadsheet was created by someone else.
Dealing with big datasets?
It’s time you learn how to code.
If you deal with large sets of data, in order to improve the quality and speed of your analysis, a good approach would be to learn how to manipulate and analyze data with powerful, and reliable tools like R, Pandas, Python, and SQL.
Even if the learning curve is steeper, in the end, it is totally worth it, the analysis possibilities are endless.
Pros of using a dedicated programming language for data analysis:
- lightning-fast results
- automation opportunities
- cleaner projects
- easy to share your scripts and collaborate with larger teams
- low error rate (code won’t even run if it finds a bug)
With coding, you can split your analysis project into clean steps, keep track of your progress, test different logics, and get reliable results for millions of rows in less than a second. Plus, you can automate tasks easily.
If you plan to use Excel as a standalone tool for analysis, you should take proper training in order to take advantage of all its features and keep the error rate at its lowest.